Papers by Mingzhou Xu

5 papers
Leveraging Local and Global Patterns for Self-Attention Networks (P19-1)

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Challenge: Existing approaches to integrate local and global information into self-attention networks have been criticized for overlooking neighboring information.
Approach: They propose a hybrid attention mechanism to leverage local and global information . they use a gating scalar to integrate both sources of information based on local contexts .
Outcome: The proposed approach improves on translation tasks and shows that the two types of contexts are complementary.
A Survey on Zero Pronoun Translation (2023.acl-long)

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Challenge: Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach: They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains .
Outcome: The proposed method and methods are compared to other models and evaluation metrics on different benchmarks.
Revisiting the Self-Consistency Challenges in Multi-Choice Question Formats for Large Language Model Evaluation (2024.lrec-main)

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Challenge: Multi-choice questions (MCQs) are a common method for assessing the world knowledge of large language models.
Approach: They propose three knowledge-equivalent question variants to assess LLMs' world knowledge . they propose option position shuffle, option label replacement, and conversion to a True/False format .
Outcome: The proposed questions are shuffle, label replacement, and True/False format.
GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

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Challenge: ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach: They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome: The proposed testset covers five genres and identifies current challenges for evaluation.
Document Graph for Neural Machine Translation (2021.emnlp-main)

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Challenge: Existing document-level NMT methods fail to leverage contexts beyond a few set of previous sentences.
Approach: They propose to represent a document as a graph that connects relevant contexts regardless of distances.
Outcome: Experiments on IWSLT English–French, Chinese-English, WMT English–German and Opensubtitle English–Russian show that using document graphs can significantly improve translation quality.

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